International Journal of Learning, Teaching and Educational Research Vol. 6, No.1, pp. 57-70, June 2014
Revising the Imaginative Capability and
Creative Capability Scales:
Testing the Relationship between Imagination
and Creativity among Agriculture Students
Yuling Hsu
National Central University Taoyuan, Taiwan
Li-Pei Peng, Jiun-Hao Wang and Chaoyun Liang
National Taiwan University Taipei, Taiwan
Abstract. We conducted three studies to revise the imaginative capability scale and creative capability scale, and to examine the relationship between imagination and creativity among college students majoring in agriculture. First, Study 1 was conducted to determine the most appropriate scale structure by performing an exploratory factor analysisof a sample comprising 390 students. A three-factor solution was used to identify the dimensions of imaginative capability (initiating, conceiving, and transforming imagination), and a two-factor solution was used to identify the dimensions of creative capability (originality and usefulness). Next, Study 2 was conducted to confirm the structures established in Study 1 by performing a confirmatory factor analysis of a sample comprising 520 college students. In addition, we tested the degree of measurement invariance of the scales across genders. Finally, Study 3 was conducted to further examine the relationship between imagination and creativity among 430 college students majoring in agriculture. The results show that originality is influenced primarily by conceiving and initiating imagination. In addition, transforming imagination has a slightly negative influence on originality. Finally, usefulness is influenced primarily by conceiving imagination, and it was slightly influenced by initiating imagination.
Keywords: agriculture students; creative capability; imaginative capability; measurement invariance; scale development.
Introduction
students. Swirski (2010) asserted that the manner in which society envisions, creates, and contributes to its educational, social, and cultural environments is limited only by human imagination. An imaginative approach to designing a learning environment frameseducational activities and facilitates innovative assessments that encourage students to explore, question, resolve, and understand the diversity and complexity of their environment. Although this research field is crucial for understanding the human ability to consider the future and promote human creativity, few studies addressed this field during the 21st century (Morosini, 2010, p. 43).
Previous studies have examined human imagination based on various approaches, such as visual imagery (Gordon, 1949; Richardson, 1969), philosophical inquiry (Emig, 1983; Warnock, 1976), spatial conceptualization (Thurstone & Thurstone, 1965), mental imagery (Marks, 1973, 1995), and imagery companions (Taylor, 1999; Taylor, Hulette, & Dishion, 2010); however, few studies have empirically examined both imaginative and creative capabilities, and few evaluation tools exist for assessing these capabilities. Liang and Chia (2014) conducted three studies to test the reliability, validity, and factor structure of the imaginative capability scale (ICS). The three-factor model of the ICS was confirmed by surveying college students studying in various domains. In addition, Lin, Hsu, and Liang (2014) developed the creative capability scale (CCS), and they examined the effect of creativity and imagination on the academic performance of design students.
Humanity is adversely affected by global trends, including unprecedented climate change, over-consumption, social inequity, and inter tribaland interreligious conflicts (Ellyard, 2011). Compared with other academic fields, agricultural education is influenced the most by these trends. The research, curricula, and teaching practices used in this field have undergone substantial changes during the past 20 years. Moreover, the number of students enrolling in agricultural colleges and universities in industrialized countries is declining, and these institutions must evaluate alternative structures and functions carefully to ensure a sustainable food supply (Lieblein, Francis, & King, 2000). Numerous scholars have stressed the need to cultivate student imagination and creativity to address the dynamic challenges associated with developing and maintaining a globally sustainable society (Ellyard, 2011; Intarachaimas, 2012).
Imagination and creativity
Imagination is defined as ―a power of the mind‖ (Perdue, 2003) that enables people to transcend experience and construct alternative possibilities to organize fragmented situations into meaningful and complete concepts (Passmore, 1985). Liu and Noppe-Brandon (2009) claimed that imagination is the ability to conceive something that either does not exist, or may exist yet cannot be perceived (p. 19). Many contemporary psychologists have described imagination as one of the ―higher mental functions‖ that ―involve the synthetic combining of aspects of memories or experiences into a mental construction that differs from past or present perceived reality and many anticipate future reality‖ (Morosini, 2010, p. 42). Furthermore, Liu and Noppe-Brandon (2009) and Liang and Chia (2014) have indicated that imaginative capability comprises three dimensions; initiating, conceiving, and transforming imagination.
Barron and Harrington (1981) noted that numerous studies have applied the following two definitions of creativity: (1) an ability manifested byperformance in critical trials (Guilford, 1975; Torrance, 1998); and (2) a socially recognized achievement that is supported through the development of novel products (Baer, Kaufman, & Gentile, 2004; Hennessey & Amabile, 2010). Regardless of which categories are applied, the concepts of originality and usefulness are considered core dimensions of creative capability (Runco & Jaeger, 2012). Many researchers have perceived originality as newness, novelty, surprise, and uncommonness (Barron, 1955; Sternberg, 1999). Similarly, usefulness has been perceived as adaptation, appropriateness, effectiveness, flexibility, practicality, utility, or value (Barron, 1988; Stein, 1953). Recently, Lin et al. (2014) empirically examined student creativity and confirmed that creative capability comprises two dimensions: originality and usefulness.
Regarding the relationship between imagination and creativity, Gaut (2005) explained that imagination can be considered a vehicle of active creativity. Gaut purported that creative people imagine various propositions and believe that subsequent developments could yield the most appropriate solution. Thus, this ―possibility thinking‖ can be considered a basis for cultivating creative thinking and driving innovation (Craft, Chappell, & Twining, 2008). Morosini (2010) analogized imagination as a conduit through which the unconscious self is expressed through creative mental imagery that can drive deliberate actions.
The need for imagination and creativity in agricultural education
Wilson and Morren (1990) reported that society demands ecological, ethical, and social dimensions to be considered in discussions on the future of agriculture and use of rural landscapes. However, the transition from focusing on agricultural productionto focusing on rural development presents difficult choices for traditional agricultural education systems. In addition to continually introducing professional knowledge and novel techniques, numerous studies have stressed the need to cultivate student imagination and creativity to address the dynamic challenges associated with developing and maintaining a globally sustainable society (Herrmann, 2011; Marshall, 2009). In particular, Marshall (2009) indicated that collective imagination is crucial to addressing widely held beliefs regarding climate change. Herrmann (2011) argued that harnessing student creativity and imagination to facilitate effective engagement in sustainable education would lead to deeper learning.
Lieblein et al. (2000) stated that the gap between knowledge and action in agricultural education must be bridged to facilitate major conceptual and structural changes that contribute constructively to a future complex and multifunctional agriculture sector, as well as to domestic food systems. Lieblein et al. (2000) hypothesized that agricultural colleges require imaginative planning and creative action to progress from being a narrowly oriented private food production sector to become a broad societal activity involving the management of natural resources and social concerns. Considerable changes are necessary regarding the management of human capital in the agriculture sector. Henry (2001) argued that creativity and innovation add more value to business than land, labor, or capital do.
Ellyard (2011) explained that a sustainable future cannot be created if it is not first imagined, and few imaginative efforts have been made to establish clear goals and objectives. Hope is the ideal quality for overcoming fear, and it is through imagination and vision that people can inspire hope; hence, an affirmative action approach to leadership thinking is necessary. Intarachaimas (2012) argued that practitioners of agricultural systems must be constantly changing, innovating, and reinventing themselves to remain competitive in an environment characterized by technological change, international competition, fluctuating consumer demand, and the pursuit for a globally sustainable society. Herrmann (2011) claimed that adopting assignments or curricular activities to harness student imagination and creativity is critical, particularly in the context of sustainable education.
Study 1: Exploratory factor analysis
The 390 participants in Study 1 were students enrolled in various programs at three Taiwanese universities. The students were recruited as a calibration sample to test the number of factors in the data by performing an exploratory factor analysis (EFA). Most participants were women (53.8%); 25.9% were freshmen, 31.3% were sophomores, 25.1% were juniors, and 17.7% were seniors. An identical investigation process was employed at each university. The surveys were conducted by graduate assistants who were accompanied by the class instructor. Participation was voluntary, confidential, and anonymous. Furthermore, the participants were allowed to review their response results.
Results
The data were analysed using SPSS version 17.0. An item analysis was performed to organize the measured items based on the formal survey data. The mean ICS scores ranged from 3.59 to 4.65, with standard deviations ranging from 0.70 to 0.99.The skewness and kurtosis values of the formal survey data ranged from −0.709 to 0.221and −0.821 to 0.928, respectively. In addition, the mean CCS scores ranged from 3.46 to 4.45, with standard deviations ranging from 0.78 to 0.99.The skewness and kurtosis of the data ranged from −0.476 to 0.228and −0.475 to 1.097, respectively. The analysis results of both scales indicated that the measured items were appropriate for further analysis.
The Kaiser–Meyer–Olkin measure of sampling adequacy was performed for the ICS (0.928) and CCS (0.917) items. In addition, the Bartlett’s test of sphericity was performed, and the results were statistically significant for both the ICS (χ2
= 55767.056, df = 351, p < .005) and CCS (χ2 = 3615.313, df = 120, p < .005) items.
Thus, the sample data were considered appropriate for factor analysis. Principal axis factoring (PAF) analysis with promax rotation was conducted to determine the dimensionality of both scales.
Based on the proven criteria (Tabachnick & Fidell, 2001), three-factor solutions (eigen values greater than one) explaining 45.56% of variance were considered the optimal factor structure for the ICS. Factor 1(i.e., conceiving imagination) included items related to sensibility, concentration, effectiveness, dialectics, and intuition; Factor 2(i.e., initiating imagination) included items related to novelty andproductivity, and Factor 3 (i.e., transforming imagination) included items related to exploration, crystallization, and transformation. The correlation coefficients among the factors ranged from 0.483 to 0.850, and the Cronbach’s α values of Factors 1–3 were 0.837, 0.891, and 0.893, respectively.
Table 1: Imaginative capability scale results
Factor／item Loading M SD
Conceiving Imagination
I become excited when I expect to be successful. .305 4.65 .96 I am emotionally stable because I expect to overcome difficulties. .316 4.21 .98 I can concentrate while thinking without being distracted. .614 3.79 .96 I can continually focus on a school project until ideas are formed. .690 3.59 .99 I can formulate an approach to meet the teacher’s requirements. .674 4.49 .83 I can formulate a needs-satisfaction approach under constraints. .657 4.40 .70 I frequently set goals in accordance with my abilities. .632 4.33 .89 I can deliberate on the contradictions of a problem to generate definite thoughts. .538 4.30 .85
I can connect seemingly unrelated concepts. .496 4.27 .80
I can absorb and integrate diverse perspectives. .495 4.62 .78 I can formulate possible approaches quickly in accordance with an assigned project. .317 4.27 .82 I can understand the implications of a concept by organizing fragmented information. .377 4.19 .84 Initiating Imagination
I formulate unique ideas more frequently than other people do. .915 4.11 .99 I frequently develop ideas by examining unconventional perspectives. .813 4.11 .99 I frequently generate new ideas by combining previous experiences. .581 4.26 .92 I frequently have a rich diversity of ideas. .835 4.15 .96 I can consistently formulate numerous approaches to complete a project. .699 3.95 .99 I am proficient at adapting valuable elements into existing concepts. .499 4.17 .91 Transforming Imagination
I frequently perceive the world through various sensorial perceptions. .462 4.47 .88 I enjoy deepening my understanding of concepts through personal experiences. .579 4.65 .95 I enjoy testing products to learn how they function. .621 4.43 .97 I can express abstract concepts by using examples from daily life. .877 4.58 .97 I can explain unfamiliar concepts and provide examples relevant to a target audience. .862 4.54 .91 I frequently use concrete images to explain difficult concepts. .854 4.50 .93 I frequently apply my experiences to resolve new problems. .748 4.65 .83 I can apply similar concepts to different school assignments. .522 4.53 .85 I can resolve daily life problems by applying what I have learned. .495 4.46 .82
Table 2: Creative capability scale results
Factor／item Loading M SD
Originality
Teachers and classmates consider that my approach to school projects is inspirational. .743 3.84 .78 Teachers and classmates consider that my approach to completing school projects is
ingenuous. .783 3.95 .78
I can generate various outcomes for a school project within a short period. .555 3.71 .94 Teachers and classmates consider that my work expresses my personal style. .721 3.98 .89 Teachers and classmates consider that I can produce work that did not exist
previously. .870 3.46 .88
Teachers and classmates consider that I can produce unique work. .878 3.63 .96 Teachers and classmates consider that my work conveys multiple meanings. .642 3.75 .81 Teachers and classmates consider that my work can stimulate diverse ideas. .808 3.67 .88 Usefulness
I can identify order in chaos. .743 4.26 .83
Study 2: Confirmatory factor analysis and measurement invariance
Method
The 520 participants in Study 2 were enrolled in various programs at five Taiwanese universities. These students were recruited as a validation sample to confirm the established factor structures by performing a CFA. Most students were men (57.1%); 36.9% were freshmen, 26% were sophomores, 27.9% were juniors, and 9.2% were seniors. In addition, we combined the sample data from Studies 1 and 2, and then selected 400 men and women to test the level of measurement invariance of the scales across genders (Vandenberg & Lance, 2000).The investigation followed the process used in Study 1.Participation was voluntary, and anonymity was guaranteed.
Results
This study tested the factorial validity of the factor structures by using LISREL version 8.80 to perform CFA with maximal likelihood estimation. Wead opted indicators recommended by Hu and Bentler (1999) and Tabachnick and Fidell (2001) to assess the goodness of fit of the model. Regarding the ICS, the three-factor solution yielded a good fit (χ2 = 1288.36, df = 321, p< .005, RMSEA = .077,
SRMR = .060, CFI = .96, NFI = .95, TLI = .96).Table 3 shows the factor loadings and composite reliability result.
Table 3: CFA results of the ICS and CCS
Variable ICS CCS
Item／Factor Conceiving imagination imagination Initiating Transforming imagination Originality Usefulness
1 0.52 0.78 0.59 0.73 0.71
2 0.51 0.72 0.68 0.72 0.74
3 0.70 0.73 0.67 0.70 0.78
4 0.73 0.77 0.68 0.71 0.73
5 0.60 0.78 0.73 0.81 0.73
6 0.73 0.74 0.67 0.78 0.71
7 0.67 0.71 0.78 0.64
8 0.74 0.67 0.83 0.48
9 0.75 0.65
10 0.63
11 0.67
12 0.66
Composite
reliability 0.897 0.888 0.882 0.916 0.882
Regarding the CCS, the two-factor solution yielded a good fit (χ2 = 528.87, df =
103, p< .005, RMSEA = .079, SRMR = .042, CFI = .97, NFI = .96, TLI = .96). Table 3 shows the factor loadings and composite reliability results. Based on the criteria reported by Hair et al. (2010), each factor achieved convergent validity. The discriminant validity results show that the φ values between Factors 1 and 2 range from 0.72 to 0.80, thereby assuring discriminant validity.
We further tested the degree of measurement invariance of both scales across genders in accordance with Steenkamp and Baumgartner (1998). As shown in Table 4, configural invariance issupported. Subsequently, we examined whether various degrees of measurement were invariant across genders, including factor loadings (metric invariance), response tendency (scalar invariance), factor covariance, factor variance, and error variance.Except forχ2 and Δχ2, which are
sensitive to large samples, other goodness of fit indices—including ΔCFI, which was proposed to test the measurement invariance—indicated that all models were acceptable under the assumption of various degrees of invariance (Cheung & Rensvold, 2002). Both the ICS and CCS exhibited a high degree of measurement invariance across genders. Furthermore, the relationship among the three covariates with three ICS factors was invariant (structural invariance). The structural invariance of the CCS was also confirmed.
Table 4: Measurement invariance tests results of the ICS and CCS
Problem χ2 Δχ2 df RMSEA NNFI CFI ΔCFI
ICS
Configural Invariance 819.9692 206 0.08620 0.9634 0.9686
Metric Invariance 831.0189 11.0497 220 0.08321 0.9659 0.9688 0.0002 Scalar Invariance 861.7500 30.7311 234 0.08167 0.9671 0.9679 -0.0009 Factor Covariance Invariance 867.4689 5.7189 235 0.08178 0.9670 0.9677 -0.0002 Factor Variance Invariance 867.9413 0.4724 237 0.08130 0.9673 0.9677 0.0000 Error Variance Invariance 927.7345 59.7932 253 0.08060 0.9673 0.9655 -0.0022 Structural Invariance 930.1504 2.4159 255 0.08027 0.9575 0.9655 0.0000 CCS
Configural Invariance 2489.2106 642 0.08682 0.9470 0.9515
Metric Invariance 2547.7527 58.5421 666 0.08605 0.9481 0.9508 -0.0007 Scalar Invariance 2728.6642 180.9115 690 0.08736 0.9458 0.9467 -0.0031 Factor Covariance Invariance 2733.3754 4.7112 693 0.08713 0.9460 0.9467 0.0000 Factor Variance Invariance 2740.0651 6.6897 696 0.08735 0.9461 0.9466 -0.0001 Error Variance Invariance 2861.4761 121.411 723 0.08800 0.9455 0.9439 -0.0027 Structural Invariance 2892.6420 31.1659 726 0.08812 0.9452 0.9434 -0.0005
Study 3: Relationship between imagination and creativity among
college students majoring in agriculture
Method
Results
This study tested the relationship between imagination and creativity by using LISREL version 8.80 to perform structural equation modeling (SEM) with maximal likelihood estimation. The results showed that the model fit was acceptable(χ2 = 4645.71, df = 851, p < .005, RMSEA = .078, SRMR = .063, CFI = .92,
NFI = .92, TLI = .93). The SEM results accounted for a substantial level variance for the dimensions of originality (R2 = .55) and usefulness (R2 = .72). Figure 1
shows the structural model.In the figure, the solid line indicates that the effect was statistically significant, whereas the dotted line indicates that the effect is not significant.
Figure 1: Structural model of the imagination and creativity of college students majoring in agriculture (n = 430)
Discussion
This study was conducted to address the problems of unbalanced items and cross-loading in the original ICS and CCS. The results of the revised ICS show that (1) the 12 items for measuring sensibility, concentration, effectiveness, dialectics, and intuition were combined into the capability of conceiving imagination; (2) the six items for measuring novelty and productivity were grouped into the capability of initiating imagination; and (3) the remaining nine items for measuring exploration, crystallization,and transformation were combined into the capability of transforming imagination. Although the problem of unbalanced items remained, the cross-loading problem was resolved. However, the revised CCS results indicated that 16 items were equally structured into two dimensions (i.e., originality and usefulness), thereby indicating that both of the aforementioned problems were resolved.
Based on the results, conceiving imagination can be defined as (1) the capability to mentally grasp the core of a phenomenon by applying intuition and sensibility; and (2) the capability to formulate effective ideas for achieving a goal through concentration and logical dialectics; in summary, it is the capability to form mental images. Next, initiating imagination can be defined as the capability to productively generate original ideas; in other words, it is the capability to initiate new ideas. Finally, transforming imagination can be defined as the capability to explore unknown concepts, crystallize abstract ideas, and recreate
Conceiving Imagination
.45
Originality
.28
.10
.65
.75
.37 .77
Initiating Imagination
Transforming Imagination
.66 -.09 Usefulness
.51
mental images based on various domains and situations; in other words, it is the capability to apply previous experience.
Although the definitions of the aforementioned imaginative capability differed slightly different from the results reported by Liang and Chia (2014), the definitions of creativity capability supported the results reported by Lin et al. (2014). Originality can be defined as the capability to generate thoughts, perform behaviors, or create works that are considered original, novel, or unique within a particular sociocultural and temporal context. In addition, usefulness can be defined as the capability to generate thoughts, perform behaviors, or create works that are considered appropriate, effective, or valuable within a specific sociocultural context, or useful to human society as a whole.
Based on the assertion that imagination functions as a vehicle of creativity, the results of this study provide a basis for empirically testing the relationship between imagination and creativity. Shin (1994) indicated that professionals in various domains may require varying levels of imagination engagement. Therefore, we considered the following three questions: (1) which imaginative capabilities trigger which creative capabilities? (2) which imaginative capabilities may be required in various domains? and (3) what are the implications of these differentiations? The answers to these questions may provide insight into student selection processes and educational strategies employed by universities, as well as the employee recruitment and incentive policies applied in creative industries.
The results of Study 3 show that both conceiving and initiating imagination exert a significant influence on originality and usefulness, whereas transforming imagination has a negative influence on originality. The results can also be interpreted from the perspective of creativity. Originality is influenced primarily by conceiving and initiating imagination. Usefulness is influenced mainly by conceiving imagination, and it is influenced slightly by initiating imagination.
The results indicate that agriculture students who are competent at initiating new ideas and forming mental images are ideal candidates for initiating school projects, and they could play a crucial role in the ideation stage. By contrast, agriculture students who are proficient at forming mental images may be ideal candidates for executing school projects, and they could play a critical role in project implementation. Furthermore, students who excel in applying their previous experiences could hinder the initiation of novel ideas or actions, potentially because most agricultural projects are inherently complex, and they have become subject to greater uncertainty because of global climate change. The findings of this study warrant further research. Furthermore, they provide insights for domains and/or settings where imaginative talent and creative performance are crucial.
Limitations and conclusion
evaluations or behavioral measures. However, the decision to use self-reporting was justified by the preliminary nature of this study. The questionnaire items used in this study were not considered personally sensitive, which can result in social desirability bias. Because this study analyzed relatively large samples, the findings are generalizable to larger populations (Chan, 2009). The consistency between the EFA and CFA results, as well as the measurement invariance results, indicate that the factor structures of the measures are stable across the sample groups, and no indications of self-reporting bias were observed.
Second, no attempt was made to examine the opinions of instructors. All participants were college students in Taiwan. Out of respect for Confucian culture, the potential influence of instructors on student imagination and creativity was not examined, which may have resulted in the limited variance observed in the sample. Future research should consider extending this study by including the perspective of instructors, as well as the influence of other contextual variables.
Despite these limitations, the results of this study provide numerous directions for future research. Lieblein et al. (2000) asserted that agricultural research and education based on narrow details invariably fails to address broad problems that determine the long-term sustainability of food systems, the environment, and rural society. The tendency to focus on researching and teaching narrow details in agricultural industries has resulted in the establishment of specialized departments and disciplines in universities (Orr, 1994). Most agricultural scientists understand neither how the components of the food system are organized, nor the long-term effect that their work has on society. Numerous scholars have stressed the need for reform in agricultural education (Corbett, 2013; Enshayan, 1992; Lacy, 1993; Lieblein et al., 2000). Accordingly, this study was conducted to build on this foundation.
We revised the ICS and CCS to provide a framework for assessing the imagination and creativity of college students majoring in agriculture. We tested the predictive relationship between imagination and creativity to facilitate an ongoing discussion on innovative reforms in agricultural education. The findings of this study support assertions made by Lieblein et al. (2000); specifically, agricultural colleges must embrace a novel rural paradigm by acknowledging that agriculture and food systems cannot be developed without referring to the intelligence, imagination, creativity, and competence of students, farmers, and consumers. Our findings also support statements made by Intarachaimas (2012); specifically, the future prosperity of human society will depend increasingly more on their capacity to develop innovative ideas to create novel agricultural products, services, technologies, and production methods, and to introduce these products and services to new markets.
Acknowledgments
The current study is part of the research project (NSC 102-2511-S-002 -009-MY2) supported by Taiwan’s National Science Council. The authors would like to acknowledge Wei-Sheng Lin for his valuable contributions in statistical analysis. The authors would also like to extend their gratitude to the insightful suggestions of anonymous IJLTER reviewers. Correspondence concerning this article should be addressed to Chaoyun Liang, Department of Bio-Industry Communication and Development, National Taiwan University, Taiwan. Electronic mail may be sent to [email protected].
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